# Connectivity-based parcellation from an existing GPe–STN tractogram: voxel-wise fingerprints and Ward clustering

**URL:** <https://community.mrtrix.org/t/connectivity-based-parcellation-from-an-existing-gpe-stn-tractogram-voxel-wise-fingerprints-and-ward-clustering/8821>\
**Category:** Uncategorized\
**Tags:** tractography\
**Created:** [August 31, 2026, 8:18am UTC](https://community.mrtrix.org/t/connectivity-based-parcellation-from-an-existing-gpe-stn-tractogram-voxel-wise-fingerprints-and-ward-clustering/8821 "2026-08-31T08:18:41Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![nicolas.tempier](https://community.mrtrix.org/user_avatar/community.mrtrix.org/nicolas.tempier/32/4809_2.png) [@nicolas.tempier](https://community.mrtrix.org/u/nicolas.tempier)\
**Post date:** [August 31, 2026, 8:18am UTC](https://community.mrtrix.org/t/connectivity-based-parcellation-from-an-existing-gpe-stn-tractogram-voxel-wise-fingerprints-and-ward-clustering/8821/1 "2026-08-31T08:18:41Z")

</div>

Hi MRtrix community,

We are currently performing an exploratory connectivity-based parcellation of the STN from an existing GPe–STN tractogram, as part of a revision of a manuscript.

The aim is to determine whether the connectivity of STN voxels with the GPe naturally supports a discrete clustering structure, and in particular whether the data support 2 or 3 connectivity-based subdivisions.

For each STN voxel, we constructed a connectivity fingerprint describing its connectivity with the GPe voxels. The tractogram itself is already genetated the idea is not to re-run tckgen for this analysis.

The fingerprints are normalized and square-root transformed, and Ward hierarchical clustering is applied directly to these feature vectors:

```auto
feats = np.sqrt(fingerprints[connected])
Z = linkage(feats, method="ward")

```

Thus, Ward operates directly on the square-root-transformed connectivity fingerprints using Euclidean geometry.

The Pearson correlation matrices shown next to the dendrograms are calculated separately and **only used for visualization** :

```auto
corr = np.corrcoef(feats)
order = leaves_list(Z)
corr_reordered = corr[np.ix_(order, order)]

```

Pearson correlation therefore does not enter the Ward clustering itself.

I have attached the results from our three independent high-resolution ex vivo specimens.

 ![image](https://community.mrtrix.org/uploads/default/original/2X/2/2b6493c8bb425901a912038fda4b2072b065eaba.jpeg)

In all three specimens, if we define the “natural K” as the number of clusters corresponding to the **largest gap between successive Ward fusion heights** , the result is _K=2_\*.

We then also cut exactly the same dendrograms at **K=3** , because three subdivisions are of particular biological interest in our study.

 ![image](https://community.mrtrix.org/uploads/default/original/2X/9/9be4cb0622f97bb556794572ca1fb66a14783af1.jpeg)

What I find interesting is that K=3 does not seem to produce three equivalent major branches. Instead, visually, the main K=2 division appears to remain and one of these two branches is subsequently subdivided:

**A / B → A / (B1 + B2)**

rather than:

**A / B / C**

The corresponding Ward-reordered fingerprint correlation matrices also seem quite different between specimens: H3H shows particularly strong block structure and even negative correlations between some groups, whereas H5H appears much more globally correlated, with H2H somewhere in between.

I would therefore be very interested in your interpretation of these plots.

Specifically:

**1. Is it reasonable to interpret the large final Ward fusion gap observed in all three specimens as evidence that the strongest level of organization is a two-cluster division?**

**2. When cutting the same hierarchical tree at K=3, is the pattern we observe reasonably described as a hierarchical subdivision of one of the two principal groups rather than evidence for three equally distinct clusters?**

**3. How much weight would you give to the block structure of the Ward-reordered Pearson correlation matrices when judging whether the clusters are meaningful?**

For example, H3H seems to show much stronger differentiation between connectivity fingerprints than H5H, despite both having K\*=2.

**4. Is the “largest successive fusion-height gap” an appropriate way to describe the natural K for Ward clustering in this context, or should this only be considered descriptive and complemented by other cluster-validity/stability measures?**

**5. Finally, can Ward fusion distances be meaningfully compared between specimens, or should their absolute magnitude only be interpreted within each dataset?**

Our current interpretation would be that these results show a reproducible **dominant bipartite organization** , with additional hierarchical structure that can generate a K=3 subdivision, rather than three equally well-separated connectivity clusters.

However, we would really appreciate opinions from people more experienced with connectivity-based parcellation and hierarchical clustering before making this interpretation in the manuscript.

Many thanks for your help.